Instructions to use horsbug98/Part_2_mBERT_Model_E2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use horsbug98/Part_2_mBERT_Model_E2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="horsbug98/Part_2_mBERT_Model_E2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("horsbug98/Part_2_mBERT_Model_E2") model = AutoModelForQuestionAnswering.from_pretrained("horsbug98/Part_2_mBERT_Model_E2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1884102bfa697f844e6198e76b6bdfded90a1810a68147935248bf7f0deba90
- Size of remote file:
- 709 MB
- SHA256:
- 1032e71e500fc9ec4d4f3946337d9c9105de0c6f043d77f4282c341dfe5143d6
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